By Enrico Formenti, Roberto Tagliaferri, Ernst Wit (eds.)
This ebook constitutes the completely refereed post-conference
proceedings of the tenth overseas assembly on Computational
Intelligence equipment for Bioinformatics and Biostatistics, CIBB 2013, held in great, France in June 2013.
The 19 revised complete papers provided have been conscientiously reviewed and
selected from 35 submissions. The papers are prepared in topical
sections on bioinformatics, biostatistics, wisdom dependent drugs, and information integration and research in omic-science.
Read Online or Download Computational Intelligence Methods for Bioinformatics and Biostatistics: 10th International Meeting, CIBB 2013, Nice, France, June 20-22, 2013, Revised Selected Papers PDF
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Additional resources for Computational Intelligence Methods for Bioinformatics and Biostatistics: 10th International Meeting, CIBB 2013, Nice, France, June 20-22, 2013, Revised Selected Papers
Development and validation of a genetic algorithm for ﬂexible docking. J. Mol. Biol. 267(3), 727–748 (1997) 10. : Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally ﬂexible docking by evolutionary programming. Chem. Biol. 2(5), 317–324 (1995) 11. 0) 12. : DrugScore CSD - knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better aﬃnity prediction. J. Med. Chem. 48(20), 6296–6303 (2005) 13.
This, as it was noted by Cheng et al. M. R. 2::HMScore MLR::XARG SVM::XAG BRT::XARG DS::Ludi2 SYBYL::ChemScore MARS::XRG RF::XRG SYBYL::PMF−Score DS::PMF DS::Jain SYBYL::G−Score kNN::XR SYBYL::D−Score C = 1 Angstrom C = 2 Angstrom C = 3 Angstrom 0 10 20 30 40 50 60 70 80 90 Success Rate (%) (a) C = 1, 2 and 3 Angstrom, N = 1 pose, Y = BA. 2::HMScore MLR::XG SYBYL::F−Score SYBYL::ChemScore MARS::XRG DS::Ludi2 SVM::AG BRT::XAG RF::XRG SYBYL::PMF−Score DS::Jain DS::PMF SYBYL::G−Score kNN::XRG SYBYL::D−Score 10 20 30 40 50 60 70 80 90 Success Rate (%) (c) C = 2 Angstrom, N = 1 pose, Y =BA.
Virtual screening with solvation and ligand-induced complementarity. In: Klebe, G. , pp. 171–190. Springer, Amsterdam (2002) 20. : A machine learning approach to predicting protein-ligand binding aﬃnity with applications to molecular docking. Bioinformatics 26(9), 1169 (2010) 21. : LeadIT, St. 1) 22. , St. 2) 23. 0) 24. : The Elements of Statistical Learning. Springer, New York (2001) 25. : R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2010) ISBN 3-900051-07-0 26.